In a wireless communication system, resource (including time, frequency, space, power and the like) allocation plays a key role in a system performance. The first-generation and second-generation mobile communication systems are generally narrowband systems, and a multi-user gain may be acquired by scheduling different users in their corresponding time slots with higher channel gains. However, since a wireless system is generally a slow fading system, time domain scheduling is in limited gains. The third-generation mobile communication system is a broadband system with stronger frequency-selective fading, and a multi-user gain is obvious as a network side may schedule users in a two-dimension: time and frequency. For example, in an LTE (Long Term Evolution) system, a system capacity and performance are greatly increased through allocating a physical resource block by a channel-dependent scheduling technology.
A multiple input multiple output (MIMO) system is capable of, using a plurality of physical antennas on a transmitter and a receiver, linearly increasing, on the premise of not increasing spectral and power resources, a multi-path fading channel capacity with the number of receiving and transmitting antennas (specifically, the minimum number of receiving or transmitting antennas), which becomes a revolutionary technology for increasing spectral efficiency. In a complex reflection environment, a channel matrix may be considered to be a full rank matrix. Through performing precoding (such as V-BLAST, D-BLAST and the like) in a transmitter or performing signal detection (such as ML, MMSE, ZF and the like) in a receiver, crosstalk among antennas may be eliminated to form multiple parallel independent sub-channels, and the total channel capacity is the sum of the channel capacities of all the sub-channels, thus breaking the limitation that the channel capacities are only logarithmically increased with power, thereby increasing the degree of spatial freedom.
The above-mentioned MIMO system only aims at a single user, a characteristic of uncorrelation of channel fading among antennas may not be always satisfied as a physical distance among array elements of array antennas is subject to many limitations, and thereby a channel capacity will be influenced. Moreover, it is not suitable to configure too many antennas as the size, cost, power consumption and the like of a user terminal are limited, and it's difficult to obtain higher capacity and performance gain on a single-user MIMO. Therefore, a multi-user MIMO (MU-MIMO) becomes an inevitable choice for further improving the performance. In an MU-MIMO system, data of a plurality of users may be spatially multiplexed on a same time frequency resource by an SDMA (Spatial Division Multiple Access) technology. However, the number of the users one time frequency resource can support is far less than the number of users need to perform transmitting, and since the channel fading of different user equipment (UE) is different from each other, the capacities and performances acquired by the different user equipment by transmitting on the same time frequency resource are entirely different. Therefore, suitable users may be selected from the users need to perform transmitting, in order to perform a user pairing, which perform data transmission on the time frequency resource jointly to acquire a multi-user gain, thus increasing the spectral efficiency of the system.
User scheduling is mostly performed by a greedy algorithm in an existing MU-MIMO system. The Greedy algorithm requires a base station, after obtaining instantaneous channel state information of users, directly calculates out, by virtue of a current channel vector of each user, a pairing relationship ensuring the best system performance, thus finding out a most suitable pairing user to perform pairing. Since pairing calculations need to be performed on channel vectors of other users according to a pairing criterion when a first user is selected, it may cost large amount of calculation and the calculation may be complex. Moreover, since a channel fading changes rapidly and randomly, once a large delay is caused due to the large amount of calculation, a system performance will be highly influenced.